Can MPT-30B-Instruct run on NVIDIA A100 80GB?

YES — Runs Great

A77Great

○Estimated from fit model

MPT-30B-Instruct needs ~54.2 GB VRAM. NVIDIA A100 80GB has 80.0 GB. With Q5_K_M quantization, expect ~81 tok/s.

Runtime: OllamaCapacity: RoomyBandwidth: HighStack: BasicBottleneck: Balanced

Operating mode

Choose the run profile you care about

Interactive favors responsiveness, while light API and scale-out lean harder on serving readiness. The fit stays the same, but the recommendation lens changes.

Current mode

Balanced

Balanced for general local use. Keeps the ranking neutral across personal and serving workflows.

Capabilities:

Select quantization to explore

Q5_K_M (High quality) — 54.2 GB, 80.9 tok/s, Runs well

54.2 GB required80.0 GB available

68% VRAM used

Fit status

Runs well

Decode

80.9 tok/s

TTFT

2394 ms

Safe context

Memory

54.2 GB / 80.0 GB

Memory breakdown

Weights21.6 GB

KV Cache23.4 GB

Runtime1.2 GB

Headroom8.0 GB

See how fast it feels

See how fast it feelsMPT-30B-Instruct on NVIDIA A100 80GB
1st promptCold start — includes initialization
>Solve: If a train travels at 60 mph for 2.5 hours, how far does it go?
Let me work through this step by step.

Given information:
 - Speed = 60 mph (miles per hour)
 - Time = 2.5 hours

Formula: Distance = Speed x Time

Calculation:
 Distance = 60 mph x 2.5 h
 = 150 miles

The train travels 150 miles.

Double-check: at 60 mph the train covers 60 miles each hour. In half an hour it covers 30 miles. So in 2.5 hours: 60 + 60 + 30 = 150 miles. Confirmed.
2nd promptWarm — model ready, no init overhead
>What if the train stops for 20 minutes halfway?
The stop doesn't change the total distance covered while moving, but it affects total trip time.

Breakdown:
 - First half: 1.25 hours at 60 mph = 75 miles
 - Stop: 20 minutes (no distance)
 - Second half: 1.25 hours at 60 mph = 75 miles

Total distance: 150 miles (same)
Total time: 2.5 h + 0.33 h = 2.83 hours
Average speed: 150 / 2.83 = 53 mph

The distance stays the same but average speed drops to 53 mph because of the stop.
Estimated: 80.9 tok/s decode · 2.4s TTFT (warm) · 202 tok/s prefill

What limits this setup

This setup is broadly balanced for this model.

No major red flags

This recommendation has enough memory headroom and acceptable estimated speed for the selected workload.

Best improvement path

Performance by workload

Workload	Grade	Fit	Decode	TTFT	Context
Chat	A	Runs well	80.9 tok/s	1306 ms	8K
Coding	A	Runs well	80.9 tok/s	2394 ms	8K
Agentic Coding	A	Runs with offload	80.9 tok/s	3482 ms	8K
Reasoning	A	Runs well	80.9 tok/s	2829 ms	8K
RAG	A	Runs with offload	80.9 tok/s	4352 ms	8K

Quantization options

How MPT-30B-Instruct (30B params) fits at each quantization level on NVIDIA A100 80GB (80.0 GB usable).

Quant	Bits	VRAM	Quality	Fit
Q2_K	2	11.7 GB	Low	B61
Q3_K_S	3	14.7 GB	Low	B62
NVFP4	4

Get started

Copy-paste commands to run MPT-30B-Instruct on your machine.

Run

docker run --rm -it ghcr.io/ggerganov/llama.cpp:full \
 --hf-repo "mosaicml/mpt-30b-instruct" \
 --hf-file "mpt-30b-instruct-Q5_K_M.gguf" \
 -c 4096 -ngl 99

Your hardware

More models your NVIDIA A100 80GB can run

Model	Params	Grade	Decode	Capabilities
👁 Mistral Devstral 2 123B Instruct	123B	A	17.6 tok/s
👁 Alibaba Qwen3-Coder 30B A3B Instruct	30.5B	S

Frequently asked questions

See all results for NVIDIA A100 80GB See all hardware for MPT-30B-Instruct

URL: https://willitrunai.com/can-run/mpt-30b-instruct-on-a100-80gb

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